





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_fnet import FNetTokenizer
else:
      lowercase_         =						None

lowercase_         =						logging.get_logger(__name__)
lowercase_         =						{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase_         =						{
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

lowercase_         =						{
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

lowercase_         =						"""▁"""
class 							SCREAMING_SNAKE_CASE  (_UpperCAmelCase ):
   _UpperCamelCase       :						List[str]					   =			VOCAB_FILES_NAMES
   _UpperCamelCase       :						Tuple					   =			PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase       :						Any					   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase       :						str					   =			['''input_ids''', '''token_type_ids''']
   _UpperCamelCase       :						List[str]					   =			FNetTokenizer


   def __init__(       self     :							Optional[Any]     ,	a     :							List[Any]=None     ,	a     :							Any=None     ,	a     :							Tuple=False     ,	a     :							Any=True     ,	a     :							List[str]=True     ,	a     :							int="<unk>"     ,	a     :							Optional[Any]="[SEP]"     ,	a     :							Any="<pad>"     ,	a     :							Union[str, Any]="[CLS]"     ,	a     :							Optional[Any]="[MASK]"     ,	**a     :							Any     ,	)->  Any:





     """simple docstring"""







     lowercase__           =    (
         AddedToken(lowerCAmelCase__     ,	lstrip=lowerCAmelCase__     ,	rstrip=lowerCAmelCase__     ,	normalized=lowerCAmelCase__			)
         if isinstance(lowerCAmelCase__     ,	lowerCAmelCase__			)
         else mask_token
     )

     super().__init__(
         lowerCAmelCase__     ,	tokenizer_file=lowerCAmelCase__     ,	do_lower_case=lowerCAmelCase__     ,	remove_space=lowerCAmelCase__     ,	keep_accents=lowerCAmelCase__     ,	unk_token=lowerCAmelCase__     ,	sep_token=lowerCAmelCase__     ,	pad_token=lowerCAmelCase__     ,	cls_token=lowerCAmelCase__     ,	mask_token=lowerCAmelCase__     ,	**lowerCAmelCase__     ,	)

     lowercase__           =    do_lower_case
     lowercase__           =    remove_space
     lowercase__           =    keep_accents
     lowercase__           =    vocab_file
     lowercase__           =    False if not self.vocab_file else True


   def 			SCREAMING_SNAKE_CASE_				(       self     :							List[Any]     ,	a     :							int     ,	a     :							int = None			)->  List[int]:





     """simple docstring"""







     lowercase__           =    [self.sep_token_id]
     lowercase__           =    [self.cls_token_id]
     if token_ids_a is None:
       return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Dict     ,	a     :							Any     ,	a     :							Tuple = None			)->  List[int]:





     """simple docstring"""







     lowercase__           =    [self.sep_token_id]
     lowercase__           =    [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep			) * [0]
     return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]


   def 			SCREAMING_SNAKE_CASE_				(       self     :							int     ,	a     :							List[str]     ,	a     :							str = None			)->  Tuple[str]:





     """simple docstring"""







     if not os.path.isdir(lowerCAmelCase__			):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
       return
     lowercase__           =    os.path.join(
         lowerCAmelCase__     ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

     if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCAmelCase__			):
       copyfile(self.vocab_file     ,	lowerCAmelCase__			)

     return (out_vocab_file,)



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				__lowercase					     =				chkpt['''model''']

				# We have the base model one level deeper than the original XLM repository
				__lowercase					     =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												__lowercase					     =				v
								else:
												__lowercase					     =				v

				__lowercase					     =				chkpt['''params''']
				__lowercase					     =				{n: v for n, v in config.items() if not isinstance(lowercase				,     (torch.FloatTensor, numpy.ndarray)				)}

				__lowercase					     =				chkpt['''dico_word2id''']
				__lowercase					     =				{s + '''</w>''' if s.find('''@@'''				) == -1 and i > 13 else s.replace('''@@'''				,     ''''''				): i for s, i in vocab.items()}

				# Save pytorch-model
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

				print(F"Save PyTorch model to {pytorch_weights_dump_path}"				)
				torch.save(lowercase				,     lowercase				)

				print(F"Save configuration file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)

				print(F"Save vocab file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__a    :	Any         =    parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)